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Spatio-Temporal Semantic Data Model for Precision Agriculture IoT Networks

Author

Listed:
  • Mario San Emeterio de la Parte

    (Departamento de Ingeniería Telemática y Electrónica (DTE), Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación (ETSIST), Universidad Politécnica de Madrid (UPM), C/Nikola Tesla, s/n, 28031 Madrid, Spain)

  • Sara Lana Serrano

    (Departamento de Ingeniería Telemática y Electrónica (DTE), Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación (ETSIST), Universidad Politécnica de Madrid (UPM), C/Nikola Tesla, s/n, 28031 Madrid, Spain)

  • Marta Muriel Elduayen

    (Departamento de Ingeniería Telemática y Electrónica (DTE), Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación (ETSIST), Universidad Politécnica de Madrid (UPM), C/Nikola Tesla, s/n, 28031 Madrid, Spain)

  • José-Fernán Martínez-Ortega

    (Departamento de Ingeniería Telemática y Electrónica (DTE), Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación (ETSIST), Universidad Politécnica de Madrid (UPM), C/Nikola Tesla, s/n, 28031 Madrid, Spain)

Abstract

In crop and livestock management within the framework of precision agriculture, scenarios full of sensors and devices are deployed, involving the generation of a large volume of data. Some solutions require rapid data exchange for action or anomaly detection. However, the administration of this large amount of data, which in turn evolves over time, is highly complicated. Management systems add long-time delays to the spatio-temporal data injection and gathering. This paper proposes a novel spatio-temporal semantic data model for agriculture. To validate the model, data from real livestock and crop scenarios, retrieved from the AFarCloud smart farming platform, are modeled according to the proposal. Time-series Database (TSDB) engine InfluxDB is used to evaluate the model against data management. In addition, an architecture for the management of spatio-temporal semantic agricultural data in real-time is proposed. This architecture results in the DAM&DQ system responsible for data management as semantic middleware on the AFarCloud platform. The approach of this proposal is in line with the EU data-driven strategy.

Suggested Citation

  • Mario San Emeterio de la Parte & Sara Lana Serrano & Marta Muriel Elduayen & José-Fernán Martínez-Ortega, 2023. "Spatio-Temporal Semantic Data Model for Precision Agriculture IoT Networks," Agriculture, MDPI, vol. 13(2), pages 1-28, February.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:2:p:360-:d:1054266
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    References listed on IDEAS

    as
    1. Raul Palma & Ioanna Roussaki & Till Döhmen & Rob Atkinson & Soumya Brahma & Christoph Lange & George Routis & Marcin Plociennik & Szymon Mueller, 2022. "Agricultural Information Model," Springer Optimization and Its Applications, in: Dionysis D. Bochtis & Claus Grøn Sørensen & Spyros Fountas & Vasileios Moysiadis & Panos M. Pardalos (ed.), Information and Communication Technologies for Agriculture—Theme III: Decision, pages 3-36, Springer.
    2. Corentin Leroux & Hazaël Jones & Léo Pichon & Serge Guillaume & Julien Lamour & James Taylor & Olivier Naud & Thomas Crestey & Jean-Luc Lablee & Bruno Tisseyre, 2018. "GeoFIS: An Open Source, Decision-Support Tool for Precision Agriculture Data," Agriculture, MDPI, vol. 8(6), pages 1-21, May.
    3. M. Safdar Munir & Imran Sarwar Bajwa & Amna Ashraf & Waheed Anwar & Rubina Rashid & Abd E.I.-Baset Hassanien, 2021. "Intelligent and Smart Irrigation System Using Edge Computing and IoT," Complexity, Hindawi, vol. 2021, pages 1-16, February.
    4. Achour, Yasmine & Ouammi, Ahmed & Zejli, Driss, 2021. "Technological progresses in modern sustainable greenhouses cultivation as the path towards precision agriculture," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
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